Calculating Spatial Risk
Concentration calculation
Convert Coordinate Reference System (CRS)
Convert data.frame to simple features (sf) object
Find highest concentration
Search for coordinates with higher concentrations within geohash
Create interactive point map
Automatically create a plot for objects obtained from highest_concentr...
Automatically create a plot for objects obtained from `find_highest_co...
Identify the focal cells exceeding the threshold
Choropleth map of an sf object with ggplot2
Create choropleth map
Determine the concentrations within the highest focal cells for the cu...
Haversine great circle distance
Find the highest concentration for the current iteration
Highest concentration risk
Splines on the sphere
Retrieve historic weather data for the Netherlands
Map point coordinates to cell indices
Create focal ("moving window") weight matrix
Automatically create a plot for objects obtained from neighborhood_gh_...
Filter observations within circle (vectorized)
Find points within a circle around a center coordinate
Map points to polygons
Identify the focal indices with the highest values
Save highest concentrations per cell for subsequent iterations
Update current focal for the next iteration
Update current rasterize for the next iteration
Provides methods for spatial risk calculations, focusing on efficient determination of the sum of observations within a circle of a given radius. These methods are particularly relevant for applications such as insurance, where recent European Commission regulations require the calculation of the maximum insured value of fire risk policies for all buildings that are partly or fully located within a 200 m radius. The underlying problem is described by Church (1974) <doi:10.1007/BF01942293>.
Useful links